AutoGen Dynamic Chat Integration

This project demonstrates how AutoGen agents can be used to dynamically execute functions through a discussion where it can be easily integrate with existing systems.

Project Overview

This application showcases the integration of AutoGen's AI agents with a Flask-based web API and a SQL Server database. It demonstrates how AI agents can:

1. Engage in dynamic conversations with users
2. Interpret user intents
3. Execute database operations based on the conversation

Current Features

• 🤖 Dynamic AI-driven chat interface
• 📊 Real-time inventory management with DataTables
• 🔄 Automatic refresh after operations
• 💾 CRUD operations through both chat and UI
• 🔍 Advanced search and filtering capabilities

Key Components

• AutoGen Agents:

  • AssistantAgent: Interprets user requests and manages the conversation flow
  • UserProxyAgent: Executes functions as directed by the AssistantAgent

• Flask API: Provides endpoints for starting chats, sending messages, and retrieving responses

• Database Integration: Uses pyodbc to connect to a SQL Server database for CRUD operations on inventory items

Features

1. Dynamic Function Execution: The AI can decide which function to call based on the conversation context.
2. Database Operations:
   • Add new items to the inventory
   • Retrieve item information
3. Conversational UI: Users interact with the system through natural language.
4. Asynchronous Processing: Utilizes asyncio for non-blocking operations.

Setup and Installation

1. Clone the repository

git clone https://github.com/ranga-tec/Autogen-dynamic-functions.git

2. Install dependencies

pip install -r requirements.txt

3. Configure your database connection in app.py

4. Run the application

python app.py

Future Enhancements

• This approach is very useful when it comes to voice control systems or for integrating voice control for existing systems.
• Easily can be extended to enhance advanced autogen AI capabilities to systems.

Credits

UI influenced by Yeyu Huang's work